Activity driven smart home system

ABSTRACT

A cloud computing server includes a memory, a processor, and a communication unit. The memory is configured to store a plurality of activities, a plurality of actions, and an association model that associates the plurality of activities with the plurality of actions. The processor is configured to determine an activity from the plurality of activities by determining a similarity between an upcoming activity and one of the plurality of activities and identify an action based on the activity and the association model. The communication unit is configured to transmit the identified action to a user equipment (UE).

TECHNICAL FIELD

This disclosure relates generally to smart home technology. Morespecifically, this disclosure relates to an activity driven smart homesystem.

BACKGROUND

The Internet, which is a human centered connectivity network wherehumans generate and consume information, is now evolving to the Internetof Things (IoT) where distributed entities, such as things, exchange andprocess information without human intervention. The Internet ofEverything (IoE), which is a combination of the IoT technology and theBig Data processing technology through connection with a cloud server,has emerged. As technology elements, such as “sensing technology”,“wired/wireless communication and network infrastructure”, “serviceinterface technology”, and “Security technology” have been demanded forIoT implementation, a sensor network, a Machine-to-Machine (M2M)communication, Machine Type Communication (MTC), and so forth have beenrecently researched.

Such an IoT environment may provide intelligent Internet technologyservices that create a new value to human life by collecting andanalyzing data generated among connected things. IoT may be applied to avariety of fields including smart home, smart building, smart city,smart car or connected cars, smart grid, health care, smart appliancesand advanced medical services through convergence and combinationbetween existing Information Technology (IT) and various industrialapplications.

SUMMARY

The present disclosure relates to a sensor network, Machine TypeCommunication (MTC), Machine-to-Machine (M2M) communication, andtechnology for Internet of Things (IoT). The present disclosure may beapplied to intelligent services based on the above technologies, such assmart home, smart building, smart city, smart car, connected car, healthcare, digital education, smart retail, security and safety services.

In a first embodiment, a cloud computing server includes a memory, aprocessor, and a communication unit. The memory is configured to store aplurality of activities, a plurality of actions, and an associationmodel that associates the plurality of activities with the plurality ofactions. The processor is configured to determine an activity from theplurality of activities by determining a similarity between an upcomingactivity and one of the plurality of activities and identify an actionbased on the activity and the association model. The communication unitis configured to transmit the identified action to a user equipment(UE).

In a second embodiment, a method for recommending one or more actionsincludes determine an activity from a plurality of activities bydetermining a similarity between an upcoming activity and one of theplurality of activities, generating an association model from a cloudbased database, and identifying an action based on the association modeland the determined activity. The method also includes transmitting therecommended one or more actions to a user equipment (UE).

In a third embodiment, a non-transitory computer readable mediumembodying a computer program having computer readable program code thatwhen executed causes at least one processing device to determine anactivity from a plurality of activities by determining a similaritybetween an upcoming activity and one of the plurality of activities,generating an association model from a cloud based database, andidentifying an action based on the association model and the determinedactivity.

Other technical features may be readily apparent to one skilled in theart from the following figures, descriptions, and claims.

Before undertaking the DETAILED DESCRIPTION below, it may beadvantageous to set forth definitions of certain words and phrases usedthroughout this patent document. The term “couple” and its derivativesrefer to any direct or indirect communication between two or moreelements, whether or not those elements are in physical contact with oneanother. The terms “transmit,” “receive,” and “communicate,” as well asderivatives thereof, encompass both direct and indirect communication.The terms “include” and “comprise,” as well as derivatives thereof, meaninclusion without limitation. The term “or” is inclusive, meaningand/or. The phrase “associated with,” as well as derivatives thereof,means to include, be included within, interconnect with, contain, becontained within, connect to or with, couple to or with, be communicablewith, cooperate with, interleave, juxtapose, be proximate to, be boundto or with, have, have a property of, have a relationship to or with, orthe like. The term “controller” means any device, system or part thereofthat controls at least one operation. Such a controller may beimplemented in hardware or a combination of hardware and software and/orfirmware. The functionality associated with any particular controllermay be centralized or distributed, whether locally or remotely. Thephrase “at least one of,” when used with a list of items, means thatdifferent combinations of one or more of the listed items may be used,and only one item in the list may be needed. For example, “at least oneof: A, B, and C” includes any of the following combinations: A, B, C, Aand B, A and C, B and C, and A and B and C.

Moreover, various functions described below can be implemented orsupported by one or more computer programs, each of which is formed fromcomputer readable program code and embodied in a computer readablemedium. The terms “application” and “program” refer to one or morecomputer programs, software components, sets of instructions,procedures, functions, objects, classes, instances, related data, or aportion thereof adapted for implementation in a suitable computerreadable program code. The phrase “computer readable program code”includes any type of computer code, including source code, object code,and executable code. The phrase “computer readable medium” includes anytype of medium capable of being accessed by a computer, such as readonly memory (ROM), random access memory (RAM), a hard disk drive, acompact disc (CD), a digital video disc (DVD), or any other type ofmemory. A “non-transitory” computer readable medium excludes wired,wireless, optical, or other communication links that transporttransitory electrical or other signals. A non-transitory computerreadable medium includes media where data can be permanently stored andmedia where data can be stored and later overwritten, such as arewritable optical disc or an erasable memory device.

Definitions for other certain words and phrases are provided throughoutthis patent document. Those of ordinary skill in the art shouldunderstand that in many if not most instances, such definitions apply toprior as well as future uses of such defined words and phrases.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of this disclosure, reference is nowmade to the following description, taken in conjunction with theaccompanying drawings, in which:

FIG. 1 illustrates an example computing system according to thisdisclosure;

FIGS. 2 and 3 illustrate example devices in a computing system accordingto this disclosure;

FIG. 4 illustrates an example smart home system according to thisdisclosure;

FIG. 5 illustrates an example method for constructing an associationmodel according to this disclosure; and

FIG. 6 illustrates an example method for recommending a set of actionbased on an activity according to this disclosure.

DETAILED DESCRIPTION

FIGS. 1 through 5, discussed below, and the various embodiments used todescribe the principles of the present invention in this patent documentare by way of illustration only and should not be construed in any wayto limit the scope of the disclosure. Those skilled in the art willunderstand that the principles of this disclosure may be implemented inany suitably arranged device or system.

FIG. 1 illustrates an example computing system 100 according to thisdisclosure. The embodiment of the computing system 100 shown in FIG. 1is for illustration only. Other embodiments of the computing system 100could be used without departing from the scope of this disclosure.

As shown in FIG. 1, the system 100 includes a network 102, whichfacilitates communication between various components in the system 100.For example, the network 102 may communicate Internet Protocol (IP)packets, frame relay frames, Asynchronous Transfer Mode (ATM) cells, orother information between network addresses. The network 102 may includeone or more local area networks (LANs), metropolitan area networks(MANs), wide area networks (WANs), all or a portion of a global networksuch as the Internet, or any other communication system or systems atone or more locations.

The network 102 facilitates communications between at least one server104 and various user equipments (UEs) 106-114. Each server 104 includesany suitable computing or processing device that can provide computingservices for one or more UEs. Each server 104 could, for example,include one or more processing devices, one or more memories storinginstructions and data, and one or more network interfaces facilitatingcommunication over the network 102.

Each UE 106-114 represents any suitable computing or processing devicethat interacts with at least one server or other computing device(s)over the network 102. In this example, the UEs 106-114 include a desktopcomputer 106, a mobile telephone or smartphone 108, a personal digitalassistant (PDA) 110, a laptop computer 112, and a tablet computer 114.However, any other or additional UEs could be used in the computingsystem 100. Additional UE according to various embodiments of thepresent disclosure may include at least one of, for example, anelectronic book reader (e-book reader), a workstation, a server, aPersonal Digital Assistant (PDA), a MPEG-1 audio layer-3 (MP3) player, amobile medical device, a camera, and a wearable device. According tovarious embodiments, the wearable device may include at least one of anaccessory type (e.g., a watch, a ring, a bracelet, an anklet, anecklace, a glasses, a contact lens, or a Head-Mounted Device (HMD)), afabric or clothing integrated type (e.g., an electronic clothing), abody-mounted type (e.g., a skin pad, or tattoo), and a bio-implantabletype (e.g., an implantable circuit).

According to some embodiments, the UE may be a home appliance. The homeappliance may include at least one of, for example, a television, aDigital Video Disk (DVD) player, an audio, a refrigerator, an airconditioner, a vacuum cleaner, an oven, a microwave oven, a washingmachine, an air cleaner, a set-top box, a home automation control panel,a security control panel, a TV box (e.g., SAMSUNG HOMESYNC, APPLE TV®,or GOOGLE TV®), a game console (e.g., XBOX® and PLAYSTATION®), anelectronic dictionary, an electronic key, a camcorder, and an electronicphoto frame.

According to another embodiment, the UE may include at least one ofvarious medical devices (e.g., various portable medical measuringdevices (a blood glucose monitoring device, a heart rate monitoringdevice, a blood pressure measuring device, a body temperature measuringdevice, etc.), a Magnetic Resonance Angiography (MRA), a MagneticResonance Imaging (MRI), a Computed Tomography (CT) machine, and anultrasonic machine), a navigation device, a Global Positioning System(GPS) receiver, an Event Data Recorder (EDR), a Flight Data Recorder(FDR), a Vehicle Infotainment Device, a UE for a ship (e.g., anavigation device for a ship or a gyro-compass), avionics, securitydevices, an automotive head unit, a robot for home or industry, anautomatic teller's machine (ATM) in banks, point of sales (POS) in ashop, or internet device of things (e.g., a light bulb, various sensors,electric or gas meter, a sprinkler device, a fire alarm, a thermostat, astreetlamp, a toaster, a sporting goods, a hot water tank, a heater, aboiler, etc.).

According to some embodiments, the UE may include at least one of a partof furniture or a building/structure, an electronic board, an electronicsignature receiving device, a projector, and various kinds of measuringinstruments (e.g., a water meter, an electric meter, a gas meter, and aradio wave meter). In various embodiments, the UE may be a combinationof one or more of the aforementioned various devices. According to someembodiments, the UE may also be a flexible device. Further, the UEaccording to an embodiment of the present disclosure is not limited tothe aforementioned devices, and may include a new UE according to thedevelopment of technology.

In this example, some UEs 108-114 communicate indirectly with thenetwork 102. For example, the UEs 108-110 communicate via one or morebase stations 116, such as cellular base stations or eNodeBs. Also, theUEs 112-114 communicate via one or more wireless access points 118, suchas IEEE 802.11 wireless access points. Note that these are forillustration only and that each UE could communicate directly with thenetwork 102 or indirectly with the network 102 via any suitableintermediate device(s) or network(s).

As described in more detail below, the computing system 100 mayautomatically identify one or more activities on a user's calendar andintelligently generate actions or action reminders corresponding to theone or more activities.

Although FIG. 1 illustrates one example of a computing system 100,various changes may be made to FIG. 1. For example, the system 100 couldinclude any number of each component in any suitable arrangement. Ingeneral, computing and communication systems come in a wide variety ofconfigurations, and FIG. 1 does not limit the scope of this disclosureto any particular configuration. While FIG. 1 illustrates oneoperational environment in which various features disclosed in thispatent document can be used, these features could be used in any othersuitable system.

FIGS. 2 and 3 illustrate example devices in a computing system accordingto this disclosure. In particular, FIG. 2 illustrates an example server200, and FIG. 3 illustrates an example UE 300. The server 200 couldrepresent the server 104 in FIG. 1, and the UE 300 could represent oneor more of the UEs 106-114 in FIG. 1.

As shown in FIG. 2, the server 200 includes a bus system 205, whichsupports communication between at least one processing device 210, atleast one storage device 215, at least one communications unit 220, andat least one input/output (I/O) unit 225.

The processing device 210 executes instructions that may be loaded intoa memory 230. The processing device 210 may include any suitablenumber(s) and type(s) of processors or other devices in any suitablearrangement. Example types of processing devices 210 includemicroprocessors, microcontrollers, digital signal processors, fieldprogrammable gate arrays, application specific integrated circuits, anddiscreet circuitry.

The memory 230 and a persistent storage 235 are examples of storagedevices 215, which represent any structure(s) capable of storing andfacilitating retrieval of information (such as data, program code,and/or other suitable information on a temporary or permanent basis).The memory 230 may represent a random access memory or any othersuitable volatile or non-volatile storage device(s). The persistentstorage 235 may contain one or more components or devices supportinglonger-term storage of data, such as a ready only memory, hard drive,Flash memory, or optical disc.

The communications unit 220 supports communications with other systemsor devices. For example, the communications unit 220 could include anetwork interface card or a wireless transceiver facilitatingcommunications over the network 102. The communications unit 220 maysupport communications through any suitable physical or wirelesscommunication link(s).

The I/O unit 225 allows for input and output of data. For example, theI/O unit 225 may provide a connection for user input through a keyboard,mouse, keypad, touchscreen, or other suitable input device. The I/O unit225 may also send output to a display, printer, or other suitable outputdevice.

Note that while FIG. 2 is described as representing the server 104 ofFIG. 1, the same or similar structure could be used in one or more ofthe UEs 106-114. For example, a laptop or desktop computer could havethe same or similar structure as that shown in FIG. 2.

As described in more detail below, the server of FIG. 2 may host a cloudbased system for driving a smart home system.

As shown in FIG. 3, the UE 300 includes an antenna 305, a radiofrequency (RF) transceiver 310, transmit (TX) processing circuitry 315,a microphone 320, and receive (RX) processing circuitry 325. The UE 300also includes a speaker 330, a main processor 340, an input/output (I/O)interface (IF) 345, a keypad 350, a display 355, and a memory 360. Thememory 360 includes a basic operating system (OS) program 361 and one ormore applications 362.

The RF transceiver 310 receives, from the antenna 305, an incoming RFsignal transmitted by another component in a system. The RF transceiver310 down-converts the incoming RF signal to generate an intermediatefrequency (IF) or baseband signal. The IF or baseband signal is sent tothe RX processing circuitry 325, which generates a processed basebandsignal by filtering, decoding, and/or digitizing the baseband or IFsignal. The RX processing circuitry 325 transmits the processed basebandsignal to the speaker 330 (such as for voice data) or to the mainprocessor 340 for further processing (such as for web browsing data).

The TX processing circuitry 315 receives analog or digital voice datafrom the microphone 320 or other outgoing baseband data (such as webdata, e-mail, or interactive video game data) from the main processor340. The TX processing circuitry 315 encodes, multiplexes, and/ordigitizes the outgoing baseband data to generate a processed baseband orIF signal. The RF transceiver 310 receives the outgoing processedbaseband or IF signal from the TX processing circuitry 315 andup-converts the baseband or IF signal to an RF signal that istransmitted via the antenna 305.

The main processor 340 can include one or more processors or otherprocessing devices and execute the basic OS program 361 stored in thememory 360 in order to control the overall operation of the UE 300. Forexample, the main processor 340 could control the reception of forwardchannel signals and the transmission of reverse channel signals by theRF transceiver 310, the RX processing circuitry 325, and the TXprocessing circuitry 315 in accordance with well-known principles. Insome embodiments, the main processor 340 includes at least onemicroprocessor or microcontroller.

The main processor 340 is also capable of executing other processes andprograms resident in the memory 360. The main processor 340 can movedata into or out of the memory 360 as required by an executing process.In some embodiments, the main processor 340 is configured to execute theapplications 362 based on the OS program 361 or in response to signalsreceived from external devices or an operator. The main processor 340 isalso coupled to the I/O interface 345, which provides the UE 300 withthe ability to connect to other devices such as laptop computers andhandheld computers. The I/O interface 345 is the communication pathbetween these accessories and the main processor 340.

The main processor 340 is also coupled to the keypad 350 and the displayunit 355. The operator of the UE 300 can use the keypad 350 to enterdata into the UE 300. The display 355 may be a liquid crystal display orother display capable of rendering text and/or at least limitedgraphics, such as from web sites.

The memory 360 is coupled to the main processor 340. Part of the memory360 could include a random access memory (RAM), and another part of thememory 360 could include a Flash memory or other read-only memory (ROM).

Although FIGS. 2 and 3 illustrate examples of devices in a computingsystem, various changes may be made to FIGS. 2 and 3. For example,various components in FIGS. 2 and 3 could be combined, furthersubdivided, or omitted and additional components could be addedaccording to particular needs. As a particular example, the mainprocessor 340 could be divided into multiple processors, such as one ormore central processing units (CPUs) and one or more graphics processingunits (GPUs). Also, while FIG. 3 illustrates the UE 300 configured as amobile telephone or smartphone, UEs could be configured to operate asother types of mobile or stationary devices. In addition, as withcomputing and communication networks, UEs and servers can come in a widevariety of configurations, and FIGS. 2 and 3 do not limit thisdisclosure to any particular UE or server.

FIG. 4 illustrates an example smart home system according to thisdisclosure. As shown in FIG. 4, the system 400 includes a cloudcomputing server 402 that may be implemented by, e.g., server 200 ofFIG. 2. Cloud computing server 402 includes a cloud based database 404that may be stored in a memory, e.g., memory 230. The cloud baseddatabase stores activity information 406 and action information 408.Activity information 406 is information regarding events that may occurin a user's professional or personal life. Action information 408 isinformation regarding interactions that may be or have been performedbetween a user and a UE. The cloud based database 404 stores activityinformation 406 and action information 408 from multiple sources, e.g.,any smart home connected to the cloud computing server 402.

The cloud computing server 402 also includes an association learningengine 410 that may be executed by, e.g., processing device 210.Association learning engine 410 constructs an association model betweenactivity information 406 and action information 408. The associationmodel characterizes a typical set of action with an activity. Theassociation learning engine 410 constructs the association model bymining a user's daily life. Specifically, the association learningengine 410 identifies feature information that includes activities,actions, and the temporal relation between the activities and theactions.

For example, in the daily family life, it is likely that similaractivities happen now and then. Moreover, among all the smart homes, onecan expect that similar activities occur in some of them. With enoughsuch occurrences, the association learning engine 410 can identify theassociation between activities and actions by adopting sequentialmining. For example, the association learning engine 410 may determinethat a UE, such as a connected car, is always refueled (i.e., an action)before a road trip (i.e., an activity), and can thus associate theaction to the activity.

The cloud computing server 402 also includes an action recommendationengine 412 that may be executed by, e.g., processing device 210. Theaction recommendation engine 412 recommends a typical set of actionsgiven an upcoming activity and the available UEs in the smart home. Whenan activity is captured by an activity detection engine 414, the actionrecommendation engine 412 will first check the cloud based database 404to see if the exact activity has happened before. If not, it will findsimilar activities. Various methods can be adopted to quantify thesimilarity between two activities. For example, by defining similarityin terms of type of activity, age, profession, and education level ofinvolved user, profile of family, the similarity can be determined usingknown filtering methods, e.g., collaborative filtering, to determine ifthe similarity between the upcoming activity and an activity stored inthe cloud based database 404 is within a predetermined range. Then, theaction recommendation engine 412 will apply the association model andidentify the typical set of associated actions with the available UEs.Because the association model is built based on crowdsourcing data, theaction recommendation engine 412 is able to suggest the most likelyactions, even if the upcoming activity is the first occurrence in aparticular home.

The activity detection engine 414 detects user's upcoming activitiesagainst a daily routine. The activity detection engine may be executedby, e.g., processing device 210 or by a processor 340 in a UE 300 asshown in FIG. 3. The activity detection engine 414 may detect bothpersonal and professional activities that will happen in the future. Forexample, by checking user's calendar, the activity detection engine 414may determine that a family member's birthday is approaching or the useris scheduled to attend a meeting the next day. The activity detectionengine 414 may detect also detect an activity based on a user input. Theactivity detection engine 414 may be based in the cloud computing server402 or may be located locally at a user's home.

The user interaction module 416 is a UE, such as UE 300, that a user mayinteract with in his or her home. Specifically, the user interactionmodule 416 may determine user's current whereabouts and then prompt therecommended actions to the user at his or her convenience. For example,this interaction can be a push notification on mobile device if it is athand or a pop-up message on smart TV if the user is watching TV. Oncethe recommended actions are either confirmed or refused by the user, theuser interaction module 416 will record the context for futureassociation learning/updating purpose and store the action in the cloudbased database 404.

A home context monitoring module 418, which may be executed by aprocessor 340 of an UE 300 as shown in FIG. 3, determines the type of UEthat are located in the home. The home context monitoring module 418also determines which users are currently in the home.

FIG. 5 illustrates an example method for constructing an associationmodel according to this disclosure. As shown in FIG. 5, the method 500begins in process 502 where the activity detection engine 414 determinesan activity and a time corresponding to the activity. In process 504,the user interaction module 416 determines an action and a timecorresponding to the action. In process 506, one or more userinteraction modules 416 determine whether there are any additionalactions being performed. If there are additional actions being performedby one or more user interaction modules 416, the next action isdetermined with a time corresponding to the action. In process 510, adetermination is made once again whether all actions have beendetermined. If all actions have not been determined, the method returnsto process 508. If all actions have been determined, the method proceedsto process 512 where the association learning engine 410 generates theassociation model by associating one or more actions with an activitybased on the time recorded for the actions and the activity and storesthe association model in the cloud based database 404.

FIG. 6 illustrates an example method for recommending a set of actionbased on an activity according to this disclosure. As shown in FIG. 6,the method 600 begins in process 602 where the activity detection engine414 determines that an upcoming activity. In process 604, the actionrecommendation engine 412 checks the cloud based database 404 for amatching activity. If there is a matching activity in the cloud baseddatabase 404, the matching activity is selected in process 606. If thereis no matching activity, the process proceeds to process 608 where theaction recommendation engine 412 searches for similar activities in thecloud based database 404. Once a similar activity is found, the similaractivity is selected by the action recommendation engine 412 in process610. In process 612, the action recommendation engine 412 determines theaction associated with the selected activity based on an associationmodel stored in the cloud based database 404. In process 614, the userinteraction module 416 recommends one or more actions to one or moreusers.

None of the description in this application should be read as implyingthat any particular element, step, or function is an essential elementthat must be included in the claim scope. The scope of patented subjectmatter is defined only by the claims. Moreover, none of the claims isintended to invoke 35 U.S.C. § 112(f) unless the exact words “means for”are followed by a participle. Use of any other term, including withoutlimitation “mechanism,” “module,” “device,” “unit,” “component,”“element,” “member,” “apparatus,” “machine,” “system,” “processor,” or“controller,” within a claim is understood by the applicants to refer tostructures known to those skilled in the relevant art and is notintended to invoke 35 U.S.C. § 112(f).

Although the present disclosure has been described with an exemplaryembodiment, various changes and modifications may be suggested to oneskilled in the art. It is intended that the present disclosure encompasssuch changes and modifications as fall within the scope of the appendedclaims.

What is claimed is:
 1. A cloud computing server comprising: a memoryconfigured to store a plurality of activities obtained from a pluralityof homes, a plurality of actions obtained from the plurality of homes,and an association model that associates the plurality of activitieswith the plurality of actions, wherein: each activity comprises anactivity time and an activity type and each action comprises an actiontime; the association model is constructed by comparing the activitytime of each of the plurality of activities obtained from the pluralityof homes with the action time of each of the plurality of actionsobtained from the plurality of homes; and the association model includesa defined activity of the plurality of activities with (i) a firstaction to be performed by a first user equipment when a first temporalrelation between the activity time of the defined activity and a firstaction time of the first action is less than a predetermined timeinterval and (ii) a second action to be performed by a second userequipment when a second temporal relation between the activity time ofthe defined activity and a second action time of the second action isless than the predetermined time interval; a processor configured to:determine a similarity between an activity type of an upcoming activityand the activity type of the defined activity, where the similarity isdetermined based on the similarity being within a predetermined range;based on the determined similarity, identify the upcoming activity ascorresponding to the defined activity; and identify the first action andthe second action associated with the defined activity based on theassociation model, the first temporal relation associated with the firstaction, and the second temporal relation associated with the secondaction; and a communication unit configured to: transmit a first signalassociated with the first action to the first user equipment, andtransmit a second signal associated with the second action to the seconduser equipment; wherein the first signal is configured to cause thefirst user equipment to perform the first action at a first time basedon the first temporal relation relative to the upcoming activity and thesecond signal is configured to cause the second user equipment toperform the second action at a second time based on the second temporalrelation relative to the upcoming activity.
 2. The cloud computingserver of claim 1, wherein the processor is configured to receive theupcoming activity via the communication unit.
 3. The cloud computingserver of claim 1, wherein the upcoming activity is determined based ona user's calendar.
 4. The cloud computing server of claim 1, wherein:the similarity is based on one or more properties of the upcomingactivity and one or more properties of the defined activity; and the oneor more properties comprise an age of a user, profession of the user,education level of the user, or profile of a family.
 5. The cloudcomputing server of claim 4, wherein the processor is configured toassign one or more actions from the plurality of actions to one or moreactivities of the plurality of activities in the association model.
 6. Amethod for recommending one or more actions, the method comprising:determining a similarity between an activity type of an upcomingactivity and an activity type of a defined activity of a plurality ofactivities obtained from a plurality of homes, where the similarity isdetermined based on the similarity being within a predetermined range;based on the determined similarity, identifying the upcoming activity asbeing the defined activity; identifying a first action and a secondaction associated with the defined activity based on an associationmodel, a first temporal relation associated with the first action, and asecond temporal relation associated with the second action, wherein: theassociation model is constructed by comparing an activity time of eachof the plurality of activities obtained from the plurality of homes withan action time of each of a plurality of actions obtained from theplurality of homes; and the association model includes the definedactivity with (i) the first action to be performed by a first userequipment when the first temporal relation between the activity time ofthe defined activity and a first action time of the first action is lessthan a predetermined time interval and (ii) the second action to beperformed by a second user equipment when the second temporal relationbetween the activity time of the defined activity and a second actiontime of the second action is less than the predetermined time interval;transmitting a first signal associated with the first action to thefirst user equipment and transmitting a second signal associated withthe second action to the second user equipment; wherein the plurality ofactivities and the plurality of actions obtained from the plurality ofhomes are stored in a cloud based database; and wherein the first signalcauses the first user equipment to perform the first action at a firsttime based on the first temporal relation relative to the upcomingactivity and the second signal causes the second user equipment toperform the second action at a second time based on the second temporalrelation relative to the upcoming activity.
 7. The method of claim 6,wherein identifying the upcoming activity comprises comparing theupcoming activity to the plurality of activities stored in the cloudbased database.
 8. The method of claim 6, wherein: the similarity isbased on one or more properties of the upcoming activity and one or moreproperties of the defined activity; and the one or more propertiescomprise an age of a user, profession of the user, education level ofthe user, or profile of a family.
 9. The method of claim 7, whereinassociations between the defined activity and the first and secondactions are stored in the cloud based database.
 10. A non-transitorycomputer readable medium embodying a computer program, the computerprogram comprising computer readable program code that when executedcauses at least one processor to: determine a similarity between anactivity type of an upcoming activity and an activity type of a definedactivity of a plurality of activities obtained from a plurality ofhomes, where the similarity is determined based on the similarity beingwithin a predetermined range; based on the determined similarity,identify the upcoming activity as being the defined activity; identify afirst action and a second action associated with the defined activitybased on an association model, a first temporal relation associated withthe first action, and a second temporal relation associated with thesecond action, wherein the association model is constructed by comparingan activity time of each of the plurality of activities obtained fromthe plurality of homes with an action time of each of a plurality ofactions obtained from the plurality of homes, wherein the associationmodel includes the defined activity of the plurality of activities with(i) the first action to be performed by a first user equipment when thefirst temporal relation between the activity time of the definedactivity of the plurality of activities and a first action time of thefirst action is less than a predetermined time interval and (ii) thesecond action to be performed by a second user equipment when the secondtemporal relation between the activity time of the defined activity anda second action time of the second action is less than the predeterminedtime interval; transmit a first signal associated with the first actionto the first user equipment; and transmit a second signal associatedwith the second action to the second user equipment; wherein theplurality of activities and a plurality of actions obtained from theplurality of homes are stored in a cloud based database; and wherein thefirst signal causes the first user equipment to perform the first actionat a first time based on the first temporal relation relative to theupcoming activity and the second signal causes the second user equipmentto perform the second action at a second time based on the secondtemporal relation relative to the upcoming activity.
 11. Thenon-transitory computer readable medium of claim 10, wherein thecomputer readable program code that when executed causes the at leastone processor to identify the upcoming activity comprises: computerreadable program code that when executed causes the at least oneprocessor to compare the upcoming activity to the plurality ofactivities stored in the cloud based database.
 12. The non-transitorycomputer readable medium of claim 10, wherein: the similarity is basedon one or more properties of the upcoming activity and one or moreproperties of the defined activity; and the one or more propertiescomprise an age of a user, profession of the user, education level ofthe user, or profile of a family.
 13. The cloud computing server ofclaim 1, wherein the processor is further configured to: determine alocation corresponding to each of the plurality of activities and eachof the plurality of actions; determine that a third user equipment iscurrently at the location corresponding to the first action; and inresponse to determining that the third user equipment is currently atthe location corresponding to the first action, control thecommunication unit to transmit a third signal associated with the firstaction to the third user equipment.
 14. The method of claim 6, furthercomprising: determining a location corresponding to each of theplurality of activities and each of the plurality of actions;determining that a third user equipment is currently at the locationcorresponding to the first action; and in response to determining thatthe third user equipment is currently at the location corresponding tothe first action, transmitting a third signal associated with the firstaction to the third user equipment.
 15. The non-transitory computerreadable medium of claim 10, wherein the computer program furthercomprises computer readable program code that when executed causes theat least one processor to: determine a location corresponding to each ofthe plurality of activities and each of the plurality of actions;determine that a third user equipment is currently at the locationcorresponding to the first action; and in response to determining thatthe third user equipment is currently at the location corresponding tothe first action, initiate transmission of a third signal associatedwith the first action to the third user equipment.
 16. The cloudcomputing server of claim 1, further comprising: an association learningengine configured to construct the association model based on temporalrelations between the plurality of activities and the plurality ofactions.
 17. The cloud computing server of claim 16, wherein theprocessor is configured to determine the similarity between the activitytype of the upcoming activity and the activity type of the definedactivity based on the association model constructed by the associationlearning engine.
 18. The method of claim 6, wherein the associationmodel is constructed by an association learning engine based on temporalrelations between the plurality of activities and the plurality ofactions.
 19. The method of claim 18, wherein the similarity between theactivity type of the upcoming activity and the activity type of thedefined activity is based on the association model constructed by theassociation learning engine.
 20. The non-transitory computer readablemedium of claim 10, wherein the computer program further comprisescomputer readable program code that when executed causes the at leastone processor to control an association learning engine to construct theassociation model based on temporal relations between the plurality ofactivities and the plurality of actions.